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Displaying 501 - 505 of 661Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change
The ground reference data obtained to assess map accuracy can be simultaneously used to estimate area (extent). This dual-purpose use of ground reference data is examined for the special case of a two-class map of ‘change’ and ‘no change’. To assess the accuracy of a change map, stratified sampling is often implemented with a disproportionately larger sample size allocated to the map change stratum. But this allocation targeting user's accuracy of change is not necessarily effective for the competing objective of estimating the area of change.
Farmers' perception of coconut mite damage and crop diversification alternatives in the coastal belt of Tanzania
This article analysed farmers' perceptions of the effects of coconut mite in their livelihood and assessed crop diversification as a copping strategy for reduced coconut production. A socio-economic model of farmers' decisions on intercropping as an indicator for overall crop diversity was developed. The study was conducted between November 2009 and March 2010 in five districts in Tanzania, which were selected on the basis of the coconut's economic importance, using structured questionnaires which were administered to 200 household heads.
Regional spatial pattern of deep soil water content and its influencing factors
Plant root systems can utilize soil water to depths of 10 m or more. Spatial pattern data of deep soil water content (SWC) at the regional scale are scarce due to the labour and time constraints of field measurements. We measured gravimetric deep SWC (DSWC) at depths of 200, 300, 400, 500, 600, 800 and 1000 cm at 382 sites across the Loess Plateau, China. The coefficient of variation was high for soil water content (SWC) in the horizontal direction (48%), but was relatively small for SWC in the vertical direction (9%).
Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests
The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm.
Employment of Indigenous Australians in the forestry sector: a case study from northern Queensland
Summary There are compelling reasons to encourage the employment of Indigenous Australians in the forestry sector. The benefits of, and constraints to, Indigenous employment in the sector were examined using a case study approach focused on Indigenous participation in ‘Operation Farm Clear’, an emergency response following Cyclone Larry in northern Queensland in 2006. The findings suggested that, given a supportive environment, there are opportunities for Indigenous people to benefit from employment in the forestry sector.